﻿<div id="principal">
<p><strong>Fabrizio Ruggeri</strong> - <a href="http://www.mi.imati.cnr.it/~fabrizio/">http://www.mi.imati.cnr.it/~fabrizio/ </a> </p>
<p>Istituto di Matematica Applicata e Tecnologie Informatiche, Milano, Itália</p>
</font>




<ul style="text-align: justify;">

<li><font face="Arial" size="2" color="#000000">
<strong>T&iacute;tulo:</strong>
</font>
</li>
<p>A Hierarchical Random-effects Model for Survival in Patients with Acute
Myocardial Infarction</p>

<li><font face="Arial" size="2" color="#000000">
<strong>Resumo:</strong>
</font>
</li>
<p id="resumo">Studies of variations in health care utilization and outcome involve
the analysis of multilevel clustered data. These analyses involve estimation
of a cluster-specific adjusted response, covariates effect and components
of variance. Beyond reporting on the extent of observed variations, these
studies examine the role of contributing factors including patients
and providers characteristics. In addition, they may assess the relationship between health-
care process and outcomes. In this talk we present a case-study, considering
firstly a Hierarchical Generalized Linear Model (HGLM) formulation, then a semi-
parametric Dirichlet Process Mixture (DPM), and propose their application to
the analysis of MOMI2 (MOnth MOnitoring Myocardial Infarction in MIlan) study
on patients admitted with ST-Elevation Myocardial Infarction diagnosys. We
develop a Bayesian approach to fitting data using Markov Chain Monte Carlo
methods and discuss some issues about model fitting.
</p>
</div>
